Post-marketing surveillance of antineoplastic agents is performed to evaluate the efficacy and safety in patients aiming at expanding drug indications and discovering potential adverse events. The real-world data is fraught with missing values. Literature addressing different strategies for dealing with missing data in such a situation is scarce. Using machine learning (ML) algorithms for predicting therapeutic outcomes of PD-1/PD-L1 Inhibitors has attracted attention. However, training a predictive model usually requires imaging or biomarker information, which is rarely available in the post-marketing surveillance data. To address these challenges, we propose an ML-aided framework to predict the outcomes of Anti-PD-1 therapy for gynecological malignancy on a dataset with 117 patient samples, treated by Camrelizumab (with 50 patient samples), Sintilimab (44), and Toripalimab (23). Four therapeutic outcomes, including Response Evaluation Criteria in Solid Tumours (RECIST), organ adverse effect (AE), general AE, and death, are predicted. The proposed framework feeds the dataset into a learning pipeline consisting of imputation, feature engineering, model training, ensemble learning, and model selection to generate the final predictive model. We conduct experiments to justify several critical design choices, such as the specific feature engineering strategies and the SMOTE over-sampling technique. The final model for each learning task is selected from a large pool of model candidates based on a joint consideration of accuracy and F1. Moreover, we conduct thorough and visualized model analysis and gain a deeper understanding of model behavior and feature importance. The results, analysis, and findings demonstrate the superiority of the proposed learning-aided framework.

Background. Cervical squamous cell carcinoma (CESC) is the gynecologic malignancy with high incidence rate and high mortality rate. Oxidative stress participates in gene regulation and malignant tumor progression, including CESC. Methods. RNA‐seq, clinical information, and genomic mutation were from The Cancer Genome Atlas‐ (TCGA‐) CESC and GSE44001 datasets. Oxidative stress‐related genes were obtained from the gene set enrichment analysis (GSEA) website. ConsensusClusterPlus was used for clustering, which was assessed by the Kaplan‐Meier (KM) survival curve analysis, mutation analysis, immunocharacteristic analysis, and therapy. Prognostic signatures were built by combining weighted correlation network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) algorithm, and stepAIC. The prognostic power of this model was evaluated using the KM survival curve analysis, receiver operating characteristic (ROC) curve analysis, nomogram, and decision curve analysis (DCA). Results. 218 of the 291 CESC cases (74.91%) presented oxidative stress‐related gene mutation, especially FBXW7. Three clusters were determined based on oxidative stress‐related genes, among which cluster 3 (C3) presented low‐frequency mutation and hyperimmune state and was sensitive to immunotherapy. This research developed a 5‐gene oxidative stress‐related prognostic signature and a RiskScore model. As shown by ROC analysis, in the TCGA and GSE44001 datasets, the RiskScore model showed a high prediction accuracy for 1‐, 3‐, and 5‐year CESC overall survival. High RiskScore was associated with enhanced immune status. The nomogram model was greatly predictive of the overall survival of CESC patients. Conclusion. Our prognostic model was based on oxidative stress‐related genes in CESC, potentially aids in CESC prognosis, and provides potential targets against CESC.

Aim. The purpose of this study was to analyse the diagnostic value of gated myocardial perfusion imaging (G-MPI) in the evaluation of myocardial injury in sarcoglycanopathy. Materials and methods. Twenty-eight patients diagnosed with sarcoglycanopathy were evaluated using 99m- -methoxyisobutylisonitrile(99Tcm-MIBI) G-MPI. The data was processed into tomographic images, and the left ventricular function was analysed using quantitative gated SPECT (QGS) to assess the degree of impairment in myocardial and cardiac function. Results. The images of 23 of the patients (82.1%) were positive. Two hundred and twenty-nine sub-segments with abnormal lesions were detected out of 391 cardiac sub-segments of these 23 positive cases. According to the segmental abnormalities, the cases were divided into two cases (8.7%) with single abnormal wall segment, six cases (26.1%) with two abnormal wall segments, and 15 cases (65.2%) with three or more abnormal wall segments or scattered lesions. Conclusions. 99Tcm-MIBI G-MPI can objectively show impaired myocardium in patients with sarcoglycanopathy. Therefore, this method is helpful for early diagnosis and follow-up of myocardial damage. Aim. The purpose of this study was to analyse the diagnostic value of gated myocardial perfusion imaging (G-MPI) in the evaluation of myocardial injury in sarcoglycanopathy. Materials and methods. Twenty-eight patients diagnosed with sarcoglycanopathy were evaluated using 99m- -methoxyisobutylisonitrile(99Tcm-MIBI) G-MPI. The data was processed into tomographic images, and the left ventricular function was analysed using quantitative gated SPECT (QGS) to assess the degree of impairment in myocardial and cardiac function. Results. The images of 23 of the patients (82.1%) were positive. Two hundred and twenty-nine sub-segments with abnormal lesions were detected out of 391 cardiac sub-segments of these 23 positive cases. According to the segmental abnormalities, the cases were divided into two cases (8.7%) with single abnormal wall segment, six cases (26.1%) with two abnormal wall segments, and 15 cases (65.2%) with three or more abnormal wall segments or scattered lesions. Conclusions. 99Tcm-MIBI G-MPI can objectively show impaired myocardium in patients with sarcoglycanopathy. Therefore, this method is helpful for early diagnosis and follow-up of myocardial damage.